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The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it and their time delays. In this paper, we present a unified pipeline for motionbased calibration of mobile platforms equipped with multiple heterogeneous sensors.We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value.We release an open-source version of our framework to the community.

Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.

Registering models is an essential building block of many robotic applications. In case of 3D data, the models to be aligned usually consist of point clouds. In this work we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative and a direct method to determine the transformation between heterogeneous scenes (solver). We analyzed the convergence behavior of this solver on synthetic data. Furthermore, we conducted comparative experiments on a full registration pipeline that operates on raw data, implemented on top of our solver. To this extent we used public benchmark datasets and we compared against state-ofthe-art approaches. Finally, we provide an implementation of our solver together with scripts to ease the reproduction of the results presented in this work.

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